Intelligent Medical Systems

Currently, patient outcome is heavily influenced by the surgical team's experience, with many potential complications being avoidable. To address such major socioeconomic problems and consistently elevate patient outcome beyond current standards, our multidisciplinary team works on intelligent medical systems in close collaboration with clinicians. Specific goals lie in the development of novel AI-enabled imaging techniques and trustworthy surgical AI systems that are ready for real-world clinical application. Key methodological challenges are related to the generalization of AI methods across devices, patient populations and hospitals, high data variability in an inherently sparse data regime, multimodal data integration, incorporation of prior knowledge into models, and the meaningful validation of AI systems under real-world clinical conditions.

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Our division strongly believes in teamwork – not as an abstract ideal, but as a lived reality every day. Members of our division are involved in projects beside their own, making our group a strong network where everyone benefits from skills and talents that complement each other.

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Are you interested in our research and would like to carry out a student research project, bachelor, master, or diploma thesis? Or would you like to write your PhD thesis in our division?

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Please refer to the following channels to get the latest news of our division:

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We are proud of the significant recognition we have achieved through various awards. These awards demonstrate the impact of our work on the scientific community and beyond.

… all awards of the division

Our engagement with the scientific community and the public is reflected in a diverse range of activities, including seminars, invited talks, conference presentations, and broad media coverage.

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The Division of Intelligent Medical Systems hosts the following seminars:

• DKFZ Data Science Seminar

The Division of Intelligent Medical Systems offers the following university lectures:

• Seminar Deep Learning in Medical Image Analysis